Abstract: The present disclosure describes design-time tools that assist a document designer in designing a document that is ready for translation into multiple target languages. In particular, techniques are described that enable a user or designer of a document to, at design time itself, check and verify that text elements included in the document for displaying text content are properly sized for displaying translations of the text content in one or more desired target languages. If a text element is not large enough to contain all the desired translations within its boundaries, i.e., there is at least one translation of the text content that cannot be fully contained within the boundaries of the text element, an indication is provided to the user or designer.

Abstract: The present disclosure describes methods, systems, and non-transitory computer-readable media for implementing a machine learning framework to generate a recommend digital assets from a digital image. For example, in one or more embodiments, the disclosed systems utilize a machine learning model to detect a shape, color, pattern, or other digital asset type from a digital image and then extract (and further modify) the detected asset type to create various different digital assets as recommendations. In some cases, the disclosed system utilizes the machine learning model to determine one or more digital asset classes associated with the digital image, generate preprocessed digital assets from the digital image for those digital asset classes, and generate production-ready digital assets from the preprocessed digital assets. Further, in some instances, the disclosed systems provide one or more of the digital assets via recommendations based on asset scores determined via the generation process.

Abstract: One or more two-dimensional images of a three-dimensional object may be analyzed to estimate a three-dimensional mesh representing the object and a mapping of the two-dimensional images to the three-dimensional mesh. Initially, a correspondence may be determined between the images and a UV representation of a three-dimensional template mesh by training a neural network. Then, the three-dimensional template mesh may be deformed to determine the representation of the object. The process may involve a reprojection loss cycle in which points from the images are mapped onto the UV representation, then onto the three-dimensional template mesh, and then back onto the two-dimensional images.

Abstract: Systems and methods for multi-view cooperative contrastive self-supervised learning, may include receiving a plurality of video sequences, the video sequences comprising a plurality of image frames; applying selected images of a first and second video sequence of the plurality of video sequences to a plurality of different encoders to derive a plurality of embeddings for different views of the selected images of the first and second video sequences; determining distances of the derived plurality of embeddings for the selected images of the first and second video sequences; detecting inconsistencies in the determined distances; and predicting semantics of a future image based on the determined distances.

Abstract: The present disclosure describes design-time tools that assist a document designer in designing a document that is ready for translation into multiple target languages. In particular, techniques are described that enable a user or designer of a document to, at design time itself, check and verify that text elements included in the document for displaying text content are properly sized for displaying translations of the text content in one or more desired target languages. If a text element is not large enough to contain all the desired translations within its boundaries, i.e., there is at least one translation of the text content that cannot be fully contained within the boundaries of the text element, an indication is provided to the user or designer.